A method for the identification and characterization of clusters of schools along the transect lines of fisheries-acoustic surveys

Abstract

Abstract The school-aggregation pattern (schools and clusters of schools) is presumed to play a significant role in determining pelagic fish-stock catchability. However, its analysis has seldom been undertaken because it requires field-behavioural data that is seldom available. Such information can now be obtained by analysing school-based data of fisheries-acoustic surveys. This paper proposes a method for doing so. The method allows for the identification of clusters of schools and the estimation of their parameters along one-dimensional, acoustic-survey transect lines. It is based on a spatial point-process approach that considers schools as point events occurring along the track sailed by a ship. More precisely, it is based on defining a maximum distance between schools in a cluster. This distance is chosen to optimize various criteria and in particular that of homogeneity concerning school location inside the clusters and school number per km. The algorithm is described and applied to a series of acoustic surveys carried out in the Bay of Biscay. The pertinence of the clusters obtained by the algorithm is evaluated by analysing which component of the spatial distribution of the schools corresponds to those clusters. This involves considering all the distances between school events and performing simulations of cluster point processes. The school clusters obtained by the proposed algorithm represent a small-range structure of a few kilometres when a longer-range structure of tens of kilometres was also present in the data.